Koh et al. BMC Genetics 2014, 15:147
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RESEARCH ARTICLE

Open Access

Genetic association between germline JAK2
polymorphisms and myeloproliferative neoplasms
in Hong Kong Chinese population: a case–control
study
Su Pin Koh1, Shea Ping Yip1*, Kwok Kuen Lee2, Chi Chung Chan3, Sze Man Lau3, Chi Shan Kho4, Chi Kuen Lau5,
Shek Ying Lin6, Yat Ming Lau6, Lap Gate Wong7, Ka Leung Au7, Kit Fai Wong8, Raymond W Chu9, Pui Hung Yu10,
Eudora YD Chow11, Kate FS Leung12, Wai Chiu Tsoi13 and Benjamin YM Yung1

Abstract
Background: Myeloproliferative neoplasms (MPNs) are a group of haematological malignancies that can be
characterised by a somatic mutation (JAK2V617F). This mutation causes the bone marrow to produce excessive
blood cells and is found in polycythaemia vera (~95%), essential thrombocythaemia and primary myelofibrosis
(both ~50%). It is considered as a major genetic factor contributing to the development of these MPNs. No genetic
association study of MPN in the Hong Kong population has so far been reported. Here, we investigated the
relationship between germline JAK2 polymorphisms and MPNs in Hong Kong Chinese to find causal variants that
contribute to MPN development. We analysed 19 tag single nucleotide polymorphisms (SNPs) within the JAK2 locus
in 172 MPN patients and 470 healthy controls. Three of these 19 SNPs defined the reported JAK2 46/1 haplotype:
rs10974944, rs12343867 and rs12340895. Allele and haplotype frequencies were compared between patients and
controls by logistic regression adjusted for sex and age. Permutation test was used to correct for multiple
comparisons. With significant findings from the 19 SNPs, we then examined 76 additional SNPs across the 148.7-kb
region of JAK2 via imputation with the SNP data from the 1000 Genomes Project.
Results: In single-marker analysis, 15 SNPs showed association with JAK2V617F-positive MPNs (n = 128), and 8 of
these were novel MPN-associated SNPs not previously reported. Exhaustive variable-sized sliding-window haplotype
analysis identified 184 haplotypes showing significant differences (P < 0.05) in frequencies between patients and
controls even after multiple-testing correction. However, single-marker alleles exhibited the strongest association

with V617F-positive MPNs. In local Hong Kong Chinese, rs12342421 showed the strongest association signal: asymptotic
P = 3.76 × 10−15, empirical P = 2.00 × 10−5 for 50,000 permutations, OR = 3.55 for the minor allele C, and 95% CI,
2.59-4.87. Conditional logistic regression also signified an independent effect of rs12342421 in significant haplotype
windows, and this independent effect remained unchanged even with the imputation of additional 76 SNPs. No significant
association was found between V617F-negative MPNs and JAK2 SNPs.
Conclusion: With a large sample size, we reported the association between JAK2V617F-positive MPNs and 15 tag JAK2 SNPs
and the association of rs12342421 being independent of the JAK2 46/1 haplotype in Hong Kong Chinese population.
Keywords: Myeloproliferative neoplasms, Janus Kinase 2 (JAK2), V617F mutation, Single nucleotide polymorphisms, Genetic
susceptibility

* Correspondence:
1
Department of Health Technology and Informatics, The Hong Kong
Polytechnic University, Hong Kong, China
Full list of author information is available at the end of the article
© 2014 Koh et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License ( which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver ( applies to the data made available in this article,
unless otherwise stated.


Koh et al. BMC Genetics 2014, 15:147
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Background
Myeloproliferative neoplasms (MPNs) are a group of
clonal diseases originating from the bone marrow. The
present study focuses on three main MPNs: polycythaemia
vera (PV), essential thrombocythaemia (ET), and primary
myelofibrosis (PMF) [1]. These three non-leukaemic

MPNs are characterised by their BCR-ABL-negativity
and recurrent genetic aberrations, particularly a somatic
mutation, JAK2V617F (hereafter V617F). This point mutation leads to the Val-to-Phe substitution at the amino acid
position 617 and constitutively activates the JAK-STAT
signalling pathway that is essential for homeostatic processes including proliferation and survival of haematopoietic cells [2,3]. It was detected in almost all PV patients
and about half of ET and PMF patients, but not in healthy
individuals [4-7]. In 2008, World Health Organization
included V617F as one of the diagnostic criteria for this
group of MPNs [1]. Subsequently, disease anticipation
was first reported in Swedish families with an increased
risk of developing MPNs among the first-degree relatives
of MPN patients [8]. Thereafter, more MPN predisposition loci were revealed by several independent groups
around the same time. It was found that the JAK2 germline haplotype 46/1 increased the likelihood of developing
MPNs, mainly in patients with the JAK2 mutation [9-15].
Association of JAK2 alleles and/or haplotypes with MPNs
has now been reported in Caucasians [9-13,16-18], Japanese [14,15], Chinese [19-22] and Brazilians [23]. However,
work remains to be done to identify the causal variants in
or flanking the JAK2 locus and to delineate the mechanism by which such casual variants contribute to MPN
development.
The aim of this study was to evaluate the association
between JAK2 germline polymorphisms and MPNs in
the Chinese population of Hong Kong. Our primary hypothesis was that the disease might have possible association with other variants spanning the JAK2 gene. Our
case–control association study was carried out in two
stages on the same sample set (n = 642): an initial direct
genotyping of 19 SNPs including the reported JAK2 46/1
risk-haplotype-tagging SNPs and other tag SNPs selected
from HapMap [24], and an imputation study of additional 76 SNPs in an attempt to narrow down the targeted region involved in the development of MPNs.
Among Asian studies, we have the largest sample size of
controls (n = 470) and the second largest total sample
size (n = 642).

Results
Participants

We recruited 172 MPN patients and 470 healthy control
subjects, all Chinese. The patients included 61 with PV,
93 with ET, 17 with PMF, and 1 with unclassified MPN,
and 86 males (50.0%) and 86 females (50.0%). Their

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mean age was 57 years (ranges: 18–88 years). For the
healthy controls, the mean age was 51 years (ranges: 16–
75 years), and there were 236 males (50.2%) and 234 females (49.8%).
Detection of JAK2V617F mutation in Hong Kong Chinese

All cases and controls were first screened for V617F mutation. Overall, 128 (74.4%) MPN patients were positive
and 44 (25.6%) negative for V617F. Age differed significantly between V617F-positive MPN cases and healthy
controls (P < 0.0001) whereas there was no difference in
age between V617F-negative MPNs and controls (P =
0.7342). However, there was still statistically significant
difference in age between all MPN cases (both V617Fpositive and -negative) and controls (P < 0.0001). Fisher’s
exact test suggested no significant difference in sex ratio
between the two groups (P > 0.3). The prevalence of
V617F in our cohort was 87% (53/61) in PV, 68% (63/
93) in ET, 65% (11/17) in PMF, and 100% (1/1) in unclassified MPN. The mutation frequency did not differ
by sex and age in our patient group. Overall, the data
suggested that MPNs can affect anyone regardless of sex
and age, in our Hong Kong Chinese population. The
mutation was not detected in the 470 healthy controls.
Genetic association study of genotyped SNPs


In total, 19 tag SNPs were selected, capturing the genetic
information of 95 SNPs in the study region (148.7 kb)
with a mean r2 of 0.96. All of them are intronic SNPs except rs3808850 (5’ upstream). As explained in the section of Materials and methods, JAK2 risk-haplotypetagging SNPs were forced to be included. The SNPs
were also called S1, S2, …., and S19 in the sequential
order from the 5’ end to the 3’ end of the JAK2 sense
strand for ease of discussion.
The genotypes were in Hardy-Weinberg equilibrium
(Fisher’s exact test P > 0.05) for all SNPs in the control
group. In general, linkage disequilibrium (LD) among
the 19 SNPs in the combined group of V617F-positive
MPN cases and healthy controls was not strong except
for those tagging the JAK2 risk-haplotype (Figure 1). The
same applied to the LD measures (r2) for the combined
group of V617F-negative MPN cases and healthy controls (details not shown).
As a difference in age was observed between cases and
controls, we sought to minimise the influence of age. To
be consistent with previous studies [13,20], we also adjusted for sex in the analyses although the difference in
sex ratio between cases and controls did not reach statistical significance. Among the five genetic models tested
(genotypic, additive, allelic, dominant and recessive) for
the 19 directly genotyped SNPs, the allelic model generated the most significant results. Therefore, we increased
the stringency of our allelic test by comparing the 19


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Figure 1 Linkage disequilibrium pattern for 19 JAK2 SNPs for V617F-positive MPN cases and healthy controls. Linkage disequilibrium
plots were generated utilising the Haploview software. The values in the boxes indicate the r2 values between the respective pairs of SNPs and

the empty boxes represent those with r2 = 1.0. Haplotype blocks were defined by solid spine of linkage disequilibrium.

SNPs between V617F-positive MPNs and controls with
adjustment for sex and age, and with correction for multiple comparisons by 50,000 permutations. All 19 SNPs
were associated with V617F-positive MPNs before permutation except rs1536798 (S5; Pasym = 0.0765) and
rs10974947 (S11; Pasym = 0.1414) while 2 other SNPs
(rs10815148 (S6) and rs3824432 (S16)) did not survive
after 50,000 permutations with Pemp > 0.05 (Table 1);
asymptotic P value is denoted as Pasym and empirical P
value as Pemp. Moreover, 8 of these 15 MPN-associated
SNPs were novel and have not been reported previously:
rs2149555 (S4), rs2149556 (S7), rs10119004 (S10),
rs12343065 (S14), rs7857730 (S15), rs7847294 (S17),
rs3780378 (S18) and rs10815162 (S19) (see footnote a of
Table 1).
The results agreed with the findings of Caucasian
studies: the minor alleles of the JAK2 risk-haplotypetagging SNPs (allele G of rs12340895 (S13), allele G of

rs10974944 (S9) and allele C of rs12343867 (S12)) were
strongly associated with V617F-positive MPNs with descending odds ratios (ORs; 3.27, 2.87, and 2.60, respectively, with Pasym ≤ 3.80 × 10−9). All 3 SNPs statistically
survived the 50,000 permutations with Pemp = 2.00 × 10−5,
which is the lowest Pemp value achievable with 50,000 permutations. These results suggested that S9, S12, and S13
were strongly associated with V617F-positive MPNs. Intriguingly, we identified rs12342421 (S8) as the most
significantly MPN-associated SNP (Pasym = 3.76 × 10−15
and Pemp = 2.00 × 10−5, Table 1) among the 19 SNPs in
Hong Kong Chinese population. The corresponding OR
for the minor allele C was 3.55 (95% CI, 2.59-4.87).
Given the significant difference between V617F-positive
MPNs and healthy controls, we then examined V617Fnegative MPN patients for the same 19 SNPs. Overall,
comparison of V617F-negative MPNs and controls did

not produce any significant association (Pemp >0.05) after


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Table 1 Allelic association tests for 19 genotyped tag SNPs of the JAK2 gene in V617F-positive MPNs
Allelesb

Genotype counts (11/12/22)

Allelic testd

Minor allele (1) freq.

a

SNP rs

1

2

Cases

Controls

Cases


Controls

OR (95% CI)

Pasym

Pemp

rs3808850 (S1)

T

A

10/53/65

66/230/174

0.2852

0.3851

0.61 (0.45-0.84)

0.0022

0.0213

rs7849191 (S2)


T

C

8/40/80

26/233/170

0.2188

0.3904

0.43 (0.30-0.60)

6.35 × 10−7

4.00 × 10−5

rs7046736 (S3)

A

C

40/69/19

65/225/180

0.5820


0.3777

2.53 (1.85-3.46)

5.92 × 10−9

2.00 × 10−5

−8

c

rs2149555 (S4)

T

C

20/85/23

41/194/235

0.4883

0.2936

2.51 (1.82-3.48)

2.01 × 10


2.00 × 10−5

rs1536798 (S5)

A

C

28/57/43

59/228/183

0.4414

0.3681

1.30 (0.97-1.74)

0.0765

0.4500
0.0509

rs10815148 (S6)

A

T

9/59/60


20/167/283

0.3008

0.2202

1.60 (1.15-2.23)

0.0057

rs2149556 (S7)

C

T

8/63/57

86/245/139

0.3086

0.4436

0.51 (0.37-0.71)

5.23 × 10−5
−15


0.0005

rs12342421 (S8)

C

G

52/54/22

43/197/230

0.6172

0.3011

3.55 (2.59-4.87)

3.76 × 10

2.00 × 10−5

rs10974944 (S9)

G

C

29/76/23


40/198/232

0.5234

0.2957

2.87 (2.08-3.96)

1.50 × 10−10

2.00 × 10−5

−6

rs10119004 (S10)

G

A

10/71/47

121/248/101

0.3555

0.5213

0.46 (0.33-0.63)


1.65 × 10

4.00 × 10−5

rs10974947 (S11)

A

G

1/31/96

15/129/326

0.1289

0.1691

0.73 (0.48-1.11)

0.1414

0.6676
−9

rs12343867 (S12)

C

T


22/80/23

39/186/245

0.4844

0.2809

2.60 (1.89-3.58)

3.80 × 10

2.00 × 10−5

rs12340895 (S13)

G

C

40/65/23

41/200/229

0.5664

0.3000

3.27 (2.37-4.51)


4.68 × 10−13

2.00 × 10−5

−10

rs12343065 (S14)

T

C

28/77/23

41/201/228

0.5195

0.3011

2.80 (2.03-3.87)

3.80 × 10

2.00 × 10−5

rs7857730 (S15)

G


T

10/61/57

89/245/136

0.3164

0.4500

0.53 (0.38-0.73)

0.0001

0.0012

rs3824432 (S16)

A

G

1/37/90

26/148/296

0.1523

0.2128


0.67 (0.45-0.98)

0.0382

0.2711

rs7847294 (S17)

A

C

2/55/71

63/240/167

0.2305

0.3894

0.39 (0.27-0.56)

3.74 × 10−7
−5

2.00 × 10−5

rs3780378 (S18)


C

T

8/58/62

84/239/147

0.2891

0.4330

0.49 (0.36-0.68)

2.25 × 10

0.0002

rs10815162 (S19)

C

G

2/43/83

40/182/248

0.1836


0.2787

0.59 (0.41-0.84)

0.0037

0.0336

Abbreviations: SNP, single nucleotide polymorphism; OR, odds ratio; Pasym, asymptotic P value; Pemp, empirical P value.
a
The SNPs are listed in sequential order from the 5’ end to the 3’ end of the sense strand of the JAK2 gene. They are also designated S1 to S19 for the sake of
easy reference and discussion. Fifteen SNPs (all except S5, S6, S11 and S16) are associated with V617F-positive MPNs. Of these 15 MPN-associated SNPs, 7 have
been reported previously (S1, S2, S3, S8, S9, S12 and S13) and 8 are novel and have not been reported previously (S4, S7, S10, S14, S15, S17, S18 and S19).
b
Alleles 1 and 2 represent the minor and major alleles of that SNP respectively. There are 128 cases and 470 controls.
c
Calculated for minor allele (allele 1) with major allele (allele 2) as the reference allele.
d
Allele frequencies were compared by logistic regression with adjustment for sex and age to give the Pasym value. Multiple comparisons were corrected by 50,000
permutations to give the Pemp value.

50,000 permutations with rs12342421 (S8) still being the
strongest SNP (Pemp = 0.0621) (Additional file 1: Table S1).
Likewise, haplotype analysis of V617F-negative MPNs did
not yield any significant results either (Pemp ≥ 0.2298; data
not shown). Nonetheless, a comparison of the SNP allele
frequencies between V617F-positive and V617F-negative
patients also did not reveal any significant difference except for rs12342421 (S8; Pasym = 0.0031 and Pemp = 0.0303)
and rs12340895 (S13; Pasym = 0.0075 and Pemp = 0.0380).
We then performed haplotype analysis by comparing

V617F-positive MPNs and controls with adjustment for
sex and age. Exhaustive variable-sized sliding-window
haplotype analysis was done on the 19 genotyped SNPs.
PLINK [25] examined 190 windows with 1 to 19 SNPs
per window, and identified 184 haplotype windows
(96.8%) showing significant differences (Pemp < 0.05) in
frequencies between patients and controls even after
50,000 permutations (Table 2). Of all the sliding haplotype windows of a given size, the haplotype window with
the most significant omnibus test is shown in the third
column from the right of Table 2. We examined such

most significant haplotype windows for all possible window sizes, and noted that all these most significant
haplotype windows always included rs12342421 (S8) as
a constituent SNP. Of all these most significant haplotype windows, the 1-SNP window rs12342421 (S8) itself
achieved the strongest association with V617F-positive
MPNs (Pasym = 3.76 × 10−15 and Pemp = 2.00 × 10−5)
(Table 2). These results were comparable to those (data
not shown) based on haplotype blocks generated from
Haploview (Figure 1).
In the 1000 Genomes Project, rs12342421 (S8) is in perfect LD (r2 = 1; Additional file 2: Figure S1A) with JAK2
risk-haplotype-tagging SNPs (rs10974944, rs12343867 and
rs12340895, i.e. S9, S12 and S13) for Han Chinese in
Beijing (CHB), and in very strong LD (r2 ≥ 0.94; Additional
file 2: Figure S1B) with these three SNPs in Caucasians of
European ancestry (CEU). All LD plots were constructed
based on solid spine of linkage disequilibrium (SSLD). The
LD was moderately strong (r2 ≥ 0.76; Figure 1) for the corresponding pairs of SNPs in our study cohort of 128
V617F-positive MPN cases and 470 controls. We found



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Table 2 Exhaustive haplotype analyses for variable-sized sliding windows across 19 genotyped JAK2 SNPs for
V617F-positive MPNsa
SW with Omnibus Test Pemp < .05

Most Significant Omnibus Test

SNPs, No.

SWs, No.

SWs, No.

First SW

Last SW

SW

Pasym

Pemp

1

19


14 b

S1

S18

S8

d

3.76 × 10−15

2.00 × 10−5

2

18

17 c

S1…S2

S18…S19

S8…S9

2.13 × 10−14

2.00 × 10−5


−14

3

17

17

S1…S3

S17…S19

S8…S10

6.33 × 10

2.00 × 10−5

4

16

16

S1…S4

S16…S19

S7…S10


8.25 × 10−13

2.00 × 10−5

−12

5

15

15

S1…S5

S15…S19

S8…S12

4.45 × 10

2.00 × 10−5

6

14

14

S1…S6


S14…S19

S8…S13

2.75 × 10−12

2.00 × 10−5

−12

7

13

13

S1…S7

S13…S19

S8…S14

2.21 × 10

2.00 × 10−5

8

12


12

S1…S8

S12…S19

S8…S15

9.00 × 10−12

2.00 × 10−5

−11

9

11

11

S1…S9

S11…S19

S6…S14

2.38 × 10

2.00 × 10−5


10

10

10

S1…S10

S10…S19

S6…S15

9.09 × 10−12

2.00 × 10−5

−11

11

9

9

S1…S11

S9…S19

S6…S16


3.03 × 10

2.00 × 10−5

12

8

8

S1…S12

S8…S19

S6…S17

6.69 × 10−11

2.00 × 10−5

−10

13

7

7

S1…S13


S7…S19

S6…S18

1.21 × 10

2.00 × 10−5

14

6

6

S1…S14

S6…S19

S6…S19

2.60 × 10−10

2.00 × 10−5

−9

15

5


5

S1…S15

S5…S19

S4…S18

3.02 × 10

2.00 × 10−5

16

4

4

S1…S16

S4…S19

S4…S19

1.97 × 10−9

2.00 × 10−5

−9


17

3

3

S1…S17

S3…S19

S3…S19

2.74 × 10

2.00 × 10−5

18

2

2

S1…S18

S2…S19

S2…S19

2.96 × 10−8


2.00 × 10−5

−8

2.00 × 10−5

19

1

1

S1…S19

S1…S19

S1…S19

6.72 × 10

Abbreviations: SNP, single nucleotide polymorphism; SW, sliding window; Pasym, asymptotic P value; Pemp, empirical P value.
a
The SW is shown as Sx…Sy, where Sx is the first SNP and Sy is the last SNP of the SW for JAK2 gene. Please refer to Table 1 for the identity of the SNP
concerned. Each sliding window was tested by an omnibus test adjusted for sex and age (implemented in PLINK). Multiple comparisons were corrected by
running 50,000 permutations to give the Pemp value. The smallest Pemp value generated after permutation is the same for all fixed-size SWs (2 × 10−5); note that
the lowest Pemp value achievable with 50,000 permutations is 2 × 10−5. The most significant results for each fixed-size SW are shown in the three rightmost
columns. Note that, among all the 190 SWs tested, S8 always appears in the most significant SW.
b
Of the nineteen SNPs tested, five (S5, S6, S11, S16, and S19) did not give Pemp < 0.05.
c

All the SWs gives Pemp < 0.05 except S5…S6.
d
Of all the 190 SWs tested, S8 (i.e. rs12342421) alone gives the most significant result for association with V617F-positive MPNs.

that rs12342421 (S8) was not in the same LD block with
JAK2 risk-haplotype-tagging SNPs in the CEU population
(Additional file 2: Figure S1B). When we further divided the
sample groups and constructed LD plots, we found that the
LD patterns, in descending order of LD strength (from the
most correlated to the least correlated), were: the controls
only ≈ the combined group of V617F-negative MPNs and
controls (Additional file 3: Figures S2 and S3, respectively),
the combined group of all MPNs and controls (Additional
file 3: Figure S4), the combined group of V617F-positive
MPNs and controls (Figure 1), all MPN cases only
(Additional file 3: Figure S5), and the V617F-positive MPN
cases only (Additional file 3: Figure S6). Overall, a higher degree of correlation was observed among these few SNP pairs
in the 1000 Genomes Project data of CHB and CEU populations (Additional file 2: Figure S1A, B) and in our controls
(Additional file 3: Figure S2) when compared with our
V617F-positive MPN cases (Additional file 3: Figure S6).

Genetic association of genotyped and imputed SNPs

With these significant findings, we further performed
imputation for 76 additional SNPs (selected using Tagger
with minor allele frequency or MAF of 0.01) with Beagle
to examine the 148.7-kb region encompassing the JAK2
locus. Manual quality control check on Beagle indicated
an accuracy of >95% in imputing the missing (removed)
genotypes. Consistent trends were identified when all 95

SNPs (19 directly genotyped and 76 imputed) were analysed together by logistic regression adjusted for sex and
age: single-marker analysis generated the strongest association signal for rs12342421 (S8) as in our initial study.
Of these 95 SNPs, 67 showed association exceeding the
significance of 8 × 10−8 (Pasym). The strongest association
was detected for rs12342421 (S8; Pasym = 3.76 × 10−15,
Pemp = 2.00 × 10−5 and OR = 3.55) while SNPs in high LD
with S8 showed similar levels of association (see Table 3
for the top 20 SNPs).


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Table 3 Logistic regression tests: Top 20 SNPs among 95 genotyped/imputed JAK2 SNPs in V617F-positive MPNs
Allelesb

Allelic Testd

Minor Allele Freq.

SNP

1

2

Cases

Controls


OR (95% CI)

Pasym

rs12342421 (S8)e

C

G

0.6172

0.3011

3.55 (2.59-4.87)

3.76 × 10−15

2.00 × 10−5

−14

a

c

Pemp

rs12347727


G

A

0.5508

0.2734

3.33 (2.43-4.56)

7.72 × 10

2.00 × 10−5

rs2225125

G

A

0.5508

0.2755

3.30 (2.41-4.53)

1.18 × 10−13

2.00 × 10−5


−13

rs1327494
rs11794778

G

A

0.5508

0.2755

3.30 (2.41-4.53)

1.18 × 10

2.00 × 10−5

T

G

0.5508

0.2798

3.26 (2.38-4.47)


2.38 × 10−13

2.00 × 10−5

−13

rs12340895 (S13)

G

C

0.5664

0.3000

3.27 (2.37-4.51)

4.68 × 10

2.00 × 10−5

rs10974914

A

G

0.5547


0.3032

3.18 (2.30-4.40)

2.46 × 10−12

2.00 × 10−5

−12

e

rs10974916

A

G

0.5547

0.3043

3.17 (2.29-4.38)

2.94 × 10

2.00 × 10−5

rs2183137


G

A

0.5508

0.2989

3.11 (2.26-4.28)

3.02 × 10−12

2.00 × 10−5

−12

rs7851556

T

C

0.5547

0.3064

3.14 (2.27-4.34)

4.79 × 10


2.00 × 10−5

rs7043489

C

A

0.5547

0.3064

3.14 (2.27-4.34)

4.79 × 10−12

2.00 × 10−5

−12

rs11794708

A

G

0.4258

0.1702


2.67 (2.02-3.53)

5.21 × 10

2.00 × 10−5

rs10974921

A

T

0.4258

0.1702

2.67 (2.02-3.53)

5.21 × 10−12

2.00 × 10−5

−11

rs7030260

A

C


0.5547

0.3202

3.05 (2.20-4.23)

2.00 × 10

2.00 × 10−5

rs10974922

T

C

0.5547

0.3213

3.04 (2.20-4.22)

2.25 × 10−11

2.00 × 10−5

−11

rs12349785


C

G

0.5195

0.2830

2.95 (2.14-4.06)

3.35 × 10

2.00 × 10−5

rs966871

T

A

0.5391

0.3021

2.86 (2.09-3.92)

5.80 × 10−11

2.00 × 10−5


−11

rs3824433
rs1159782
e

rs10974944 (S9)

T

C

0.5312

0.3000

2.88 (2.09-3.95)

7.73 × 10

2.00 × 10−5

C

T

0.5195

0.2936


2.89 (2.09-3.99)

1.16 × 10−10

2.00 × 10−5

−10

2.00 × 10−5

G

C

0.5234

0.2957

2.87 (2.08-3.96)

1.50 × 10

Abbreviations: SNP, single nucleotide polymorphism; OR, odds ratio; Pasym, asymptotic P value; Pemp, empirical P value.
a
The SNPs are listed in ascending order in terms of their Pasym among the top 20 most significantly associated JAK2 SNPs in V617F-positive MPN patients.
Association was tested by logistic regression with adjustment for sex and age.
b
Alleles 1 and 2 represent the minor and major alleles of that SNP respectively. There are 128 cases and 470 controls.
c
Calculated for minor allele (allele 1) with major allele (allele 2) as the reference allele.

d
Allele frequencies were calculated by logistic regression with sex and age as covariates to give the Pasym value. Multiple comparisons were corrected by 50,000
permutations to give the Pemp value.
e
These three SNPs (S8, S9 and S13) were directly genotyped in this study while the rest were imputed by Beagle v3.2 [41].

To have an overall picture, we examined the LD structure (Figure 2) for all 95 SNPs (19 directly genotyped
and 76 imputed). We realised that rs12342421 (SNP no.
43 in Figure 2) also tagged (r2 = 0.85) rs4495487 (SNP
no. 49 in Figure 2) that was reported to be the additional
variant contributing to MPN predisposition in Japanese
population [14]. All the SNPs within this haplotype
block showed very strong extent of LD (r2 close to 1;
bottom panel of Figure 2).
Likewise, exhaustive haplotype analysis was performed
on these 95 SNPs to further restrict the linked region
and identify the most probable MPN-predisposing variants or haplotypes (Additional file 4: Table S2). Age and
sex were adjusted as covariates. The SNP rs12342421
(S8) again topped the 1634 haplotype windows as a 1SNP window (S8 itself ): Pasym = 3.76 × 10−15 and Pemp =
2.00 × 10−5 for 50,000 permutations (Additional file 4:
Table S2). Adjacent SNPs spanning across rs12342421
formed the most significantly associated haplotypes
among the rest as in the sliding windows for the 19

directly genotyped SNPs. The SNP rs12342421 (S8) was
obviously important because almost all the statistically
significant haplotypes carried this SNP.
Conditional logistic regression

Based on the results from PLINK, we tested the individual effect on disease association of the strongest MPNassociated SNP (rs12342421, i.e. S8) and the riskhaplotype-tagging SNPs (rs10974944, rs12343867 and

rs12340895, i.e. S9, S12 and S13) in the corresponding
sliding window. The shortest and most significant sliding
haplotype window containing these four SNPs was the
6-SNP window S8…S13 (Pasym = 2.75 × 10−12; Table 2),
which was therefore selected for conditional logistic regression analysis. Conditional analysis for the independent effect of one SNP at a time suggested that only
rs12342421 (S8) contributed an independent effect to
the significant association between the 6-SNP window
and V617F-positive MPN cases (P = 0.0005 for omnibus
test of independent effect, Table 4). Logically, controlling


Koh et al. BMC Genetics 2014, 15:147
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Page 7 of 12

Figure 2 Linkage disequilibrium pattern for 95 JAK2 SNPs for V617F-positive MPN cases and healthy controls. Linkage disequilibrium
plots were generated utilising the Haploview software. The values in the boxes indicate the r2 values between the respective pairs of SNPs and
the empty boxes represent those with r2 = 1.0. Haplotype blocks were defined by solid spine of linkage disequilibrium.

for all the single SNPs except rs12342421 (S8) yielded a reduced but still statistically significant P value of ≤0.0072
while controlling for rs12342421 (S8) demolished the significance (P = 0.4360) (Table 4). In other words, we could
not detect any significant association when rs12342421
(S8) was removed from the combination, and the original
risk-haplotype-tagging SNPs (S9, S12 and S13) did not explain all the association signals.
Our data suggested that JAK2 germline polymorphisms,
especially rs12342421 (S8), were significantly associated
with V617F-positive MPN in Hong Kong Chinese
population.

Discussion

There has been evidence suggesting that JAK2 46/1
haplotype contributed to the development of V617F-positive MPNs, but the findings for V617F-negative MPNs are
inconsistent and less convincing. While most of the studies detected no association between the risk-haplotype and
V617F-negative MPNs [9,10,17,20-22], significant association with V617F-negative MPN patients was reported
in two studies with bigger sample size (n = 108 and 53)
[12,13]. In the light of recent Chinese studies that the
JAK2 haplotype poses a higher risk of developing V617Fpositive MPNs [19,20], we employed a case–control study


Koh et al. BMC Genetics 2014, 15:147
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Page 8 of 12

Table 4 Conditional haplotype-based test: independent effects of individual JAK2 SNPs on the 6-SNP sliding window
S8…S13a
Conditional haplotype-based association test, P value
Sxb

Independent effect of Sxc

Controlling for Sxd

rs12342421 (S8)

0.0005

0.4360

rs10974944 (S9)


–

0.0072

rs10119004 (S10)

0.4700

2.84 × 10−7

rs10974947 (S11)

0.2480

1.79 × 10−14

rs12343867 (S12)

0.7970

0.0019

rs12340895 (S13)

–

0.0072

e


e

a

This table shows the individual effects of the constituent single nucleotide polymorphisms (SNPs) on the shortest and most significant sliding window that
contains the most impressive SNP in our study (rs12342421, i.e. S8) and the risk-haplotype-tagging SNPs (rs10974944, rs12343867 and rs12340895, i.e. S9, S12 and
S13). Conditional logistic regression was performed with adjustment for sex and age. The shortest and most significant sliding window carrying these four SNPs is
S8…S13 (see Table 2). The conditional omnibus test invoked by the “--chap” command of PLINK gives a P value of 1.34 × 10−14 (based on likelihood ratio test).
Note that this P value is similar, but not identical, to the P value of 2.75 × 10−12 (based on Wald test, Table 2) generated by the omnibus test of logistic regression
invoked by the “--logistic” command of PLINK in the sliding-window approach.
b
Sx indicates the SNP tested for an independent effect one at a time by the conditional haplotype-based analysis of the sliding window S8…S13. Please refer to
Table 1 for the identity of the SNPs concerned.
c
Omnibus P value for the effect of Sx that is independent of the other SNPs in the sliding window S8…S13.
d
Omnibus P value for the sliding window S8…S13 when Sx is controlled for.
e
Not a valid comparison due to identical alternate and null models

design to explore the described genetic susceptibility to
MPNs in the Hong Kong Chinese population. To avoid
missing any potential causal variant in the region, we investigated not only the risk-haplotype-tagging SNPs but
also a total of 95 SNPs in two stages with an increased
sample size. In the first stage, we genotyped 19 tag SNPs
of the JAK2 locus. In the second stage, we carried out
genotype imputation on additional 76 JAK2 SNPs. We
then combined the 19 directly genotyped SNPs and the 76
imputed SNPs (95 in total), and carefully examined both
datasets by both single-marker and haplotype analyses.

After single-marker analysis, we adopted a variablesized sliding-window strategy to examine haplotypic effects in an unbiased manner. This exhaustive approach
is best suited for capturing the haplotypes of all possible
sliding-widow sizes (including single markers) that are
most significantly associated with MPNs [26]. This comprehensive approach identified from the 19 directly genotyped SNPs 184 haplotype windows that showed
significant association (~97% of all 190 haplotype windows; Pemp < 0.05, Table 2) even after correction for multiple comparisons. However, single-marker analyses of
both the 19 SNPs and the 76 imputed SNPs showed that
V617F-positive MPNs were associated more significantly
with the single SNP rs12342421 (S8, also tagging the risk
haplotype) than the haplotypes (Table 1 vs Table 2, and
Table 3 vs Additional file 4: Table S2)) although strong
association between the risk-haplotype-tagging SNPs
(rs10974944, rs12343867 and rs12340895, i.e., S9, S12
and S13) and V617F-positive MPNs was also evident.
The C allele rs12342421 (S8) was enriched in V617Fpositive MPN patients when compared with controls.
Our conditional logistic regression further demonstrated
that this single SNP contributed an independent effect to

the most significant association between haplotypes and
MPNs (Table 4) – a novel finding not previously reported.
Analysis showed that rs12342421 (S8) had stronger association when it was not combined with other SNPs, i.e. as a
single marker (Table 2). This means that the effect of
rs12342421 (S8) became less significant when it was combined with other SNPs. The results also imply that the original risk-haplotype-tagging SNPs (S9, S12 and S13) do
not explain all the association signals; this finding is intriguing because many studies only focused on one or more
of these three risk-haplotype-tagging SNPs.
Although rs12342421 (S8) was analysed in an early study,
the results were never reported explicitly [10]. Two other
studies indeed reported the association of rs12342421 (S8)
with MPNs in Caucasians [16,27]. However, both studies
did not investigate whether rs12342421 (S8) contributed an
effect independent of the JAK2 46/1 haplotype [16,27]. In

addition, Pardanani et al. [16] is so far the only study that
reported opposite effects (high-risk vs protective) for PV
and ET for SNPs found to be associated with these MPN
subtypes. Zerjavic et al. [27] is so far also the only study that
failed to demonstrate association between MPNs and
rs12343867 (S12) – the SNP most commonly used for
tagging the 46/1 haplotype, while other risk-haplotype
tagging SNPs still showed association with MPNs.
Zerjavic et al. [27] also reported a less significant association for rs12342421 (S8) than for rs10974944 (S9) –
a finding different from ours (Table 1).
Overall, 19 tag SNPs were genotyped in this study and
15 found to be associated with V617F-postive MPNs
(see footnote a of Table 1). Of these, 7 have been previously reported to be associated with MPNs in one or more
studies [9-23], including the most three commonly studied
risk-haplotype-tagging SNPs rs10974944, rs12343867 and


Koh et al. BMC Genetics 2014, 15:147
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rs12340895 (i.e. S9, S12 and S13). The remaining eight
SNPs are novel MPN-associated SNPs and have not been
reported previously. In contrast, four SNPs that have been
reported to be associated MPN or its subtypes were not
genotyped experimentally or by imputation in the current
study: rs10758677 in a European study [9], rs10758669 in
an American study [16], rs11999802 in another American
study [18] and rs10118930 in a Chinese study [21]. Of particular interest is rs11999802, a genome-wide significant
SNP (P = 1.8 × 10−8) associated with PV with an allelic OR
of 4.41 in a small-scale genome-wide association study
involving 34 PV patients and 3,278 control subjects of

European ancestry [18].
Our results show that the significant association between
JAK2 polymorphisms and MPNs in Hong Kong Chinese is
comparable to the results in other populations. However,
we found that rs12342421 (S8) was not in the same LD
block with JAK2 risk-haplotype-tagging SNPs in the CEU
population (Additional file 2: Figure S1B) although it is still
in strong LD (r2 close to 1) with JAK2 risk-haplotypetagging SNPs. This may explain why rs12342421 (S8), rather than the JAK2 risk-haplotype-tagging SNPs, exhibits a
stronger association with MPNs in our population. When
we examined the effect of V617F on the extent of LD,
we found that the r2 between rs12342421 (S8) and other
JAK2 risk-haplotype-tagging SNPs decreased in a V617Fdependent manner. We observed that controls had stronger LD (r2) among these SNPs than cases, and that cases
without V617F mutation had stronger LD than cases with
V617F mutation (Additional file 3: Figures S2-S6 and
Figure 1). The r2 values were much lower when V617Fpositive cases were included to construct the LD plot. It
has been demonstrated that there can be extensive variation in the extent of LD between cases and controls in
a region of genetic association [28]. The variation in LD
patterns observed in our cases (especially cases with
V617F) and controls suggests that the region surrounding rs12342421 (S8) is associated with MPNs. While
current genetic maps can be used to examine the LD
structure, fine mapping at higher resolution may still be
required to sufficiently examine the region because recombination occurs not only in hot spots [29].
We explored the potential biological functions of the
genotyped genetic markers with several web-based SNP
prediction tools that are supported by regularly updated
databases and software tools: SNPnexus [30], SNP Function Prediction (FuncPred) [31], F-SNP [32] and MaInspector [33]. In silico analysis predicted no known
function for rs12342421 (S8) and other genotyped SNPs
except that one 5’-upstream SNP (rs3808850 (S1)) and
two intronic SNPs (rs7849191 (S2) and rs3780378 (S18))
were predicted by FuncPred to be involved in transcription factor binding sites. Experimental functional studies

may be required to clarify this issue.

Page 9 of 12

We then conducted an analysis of expression quantitative trait loci (eQTL) across the JAK2 gene (142.8 kb)
with several online tools: eQTL resources @ the Pritchard
lab [34], seeQTL [35], and UCSC Genome Browser [36].
This analysis did not detect any regulatory regions within
the two recombination hotspots encompassing the JAK2
gene [9].
These circumstantial findings suggest that the causal
variants driving the disease development may not be the
SNPs or haplotypes reported here, but some untyped
variants in LD with these markers. However, it is also
possible that the potential functions of the associated
SNPs are some biological processes that are not well
captured by current functional annotation software.
Owing to limited eQTL studies on different tissues or
cell types, eQTL studies might provide only limited
knowledge for linking regulatory variants to specific
genes in different tissues or cell types. There might be
some other eQTLs that have not been curated, leading
to the limited information [37].
The distribution of V617F in our Hong Kong MPN patients (PV, ET and PMF) is similar to those in other
studies [4-7]. This justified our comparable findings to
those in other populations. Taken together, our results
corroborate the findings that JAK2 variants are predisposing factors for MPN development dependent on
V617F in Hong Kong Chinese, especially rs12342421
(S8). Conceivably, the failure to detect, in our study, the
association between V617F-negative MPNs and controls

as reported elsewhere [12,13] can be ascribed to the
small sample size of the cases (n = 44). Larger sample
size would probably be needed to detect an association
for V617F-negative MPNs.
To the best of our knowledge, we are the first to perform genotype imputation in genetic association studies of
MPNs. Being an essential component in genetic association study, imputation enabled us to test many untyped
markers for associations with MPNs and hence increased
the chance to identify causal variants. Although we failed
to find the causal variant, imputation together with conditional logistic regression indeed further strengthens our
confidence to conclude that rs12342421 (S8) contributed
an independent effect to the most significant association
between JAK2 risk haplotype and MPNs.

Conclusions
Fifteen JAK2 germline polymorphisms were associated
with MPN patients with V617F mutation in Hong Kong
Chinese population. The single JAK2 SNP rs12342421
(S8) was associated with predisposition to the development of V617F-positive MPN by 3.55 fold for the minor
allele C, but independent of the JAK2 46/1 haplotype. No
significant association was found between V617F-negative
MPN patients and the JAK2 risk alleles. We have


Koh et al. BMC Genetics 2014, 15:147
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presented some plausible arguments that S8 is likely to
be involved in the pathogenesis of MPN. However, further functional validation is necessary to prove its involvement in the disease development.

Methods
Subjects and DNA samples


Participants were Hong Kong Chinese MPN patients diagnosed according to the WHO 2008 criteria [1] and recruited from six local hospitals. Every patient signed a
written informed consent. Both blood and saliva samples
were collected from patients. Blood DNA was extracted
with FlexiGene DNA Kit (Qiagen) and used for V617F detection. Saliva samples were collected using the Oragene
DNA self-collection kit (DNA Genotek), and saliva DNA
was extracted according to the manufacturer’s instructions
and used for SNP genotyping. As for controls, 470 blood
samples from anonymous healthy Chinese donors were
collected from the Hong Kong Red Cross Blood Transfusion Service and these donors were matched to the MPN
patients for sex and age as much as possible. DNA extracted from control blood samples were used for both
V617F detection and SNP genotyping. Assuming a prevalence of 0.00002, MAF of 0.1, genotypic relative risk of 2.5
for Aa and 5.0 for AA, we estimated that a sample size of
128 cases and 460 controls would have 80% power (Genetic Power Calculator) [38]. This study was approved by
the Human Subjects Ethics Sub-Committee of the University (reference numbers: 20090801001 and 20111118001)
and Research Ethics Committees of the hospitals, according to the guideline of the Declaration of Helsinki.
The Research Ethics Committees of the hospitals under
Hospital Authority included the following: Kowloon West
Cluster Clinical Research Ethics Committee (reference
number: KW/EX/09-076); Research Ethics Committee,
Kowloon Central/Kowloon East Clusters (KC/KE-090120/FR-3); Joint The Chinese University of Hong
Kong–New Territories East Cluster Clinical Research
Ethics Committee (reference number: CRE-2009.423); and
Ethics Committee, Hong Kong Easter Cluster (HKEC2009-069). All experiments were performed in the research laboratories of Department of Health Technology
and Informatics, The Hong Kong Polytechnic University.
JAK2V617F mutation analysis

DNA from all blood samples of patients and controls
were tested for V617F by amplification refractory mutation system modified from Jones et al. [4]. PCR products
were analysed by electrophoresis on 5% polyacrylamide

gels. Details are provided in Additional file 5.
SNP selection and genotyping

First, we attempted to identify JAK2 germline variants
that are associated with the development of MPNs in

Page 10 of 12

our Hong Kong Chinese population in addition to the
JAK2 risk haplotypes. Tag SNPs were selected using the
Tagger software from a 148.7-kb region encompassing
the JAK2 locus and its potential regulatory regions (3 kb
upstream and downstream of JAK2) with MAF ≥0.1 and
pairwise tagging algorithm, r2 ≥ 0.8, based on HapMap
CHB database (release #24/phase I) [24]. In line with
previous studies, we force-included the reported riskhaplotype-tagging SNPs (rs10974944, rs12343867, and
rs12340895; i.e. S9, S12 and S13) [9-11,17]. To avoid the
complication from loss of heterozygosity resulting from
somatic isodisomy in clonal myeloid cells, DNA from patients’ saliva samples (instead of blood samples) was used
for SNP genotyping. In this study, two methods were used
for genotyping SNPs (Additional file 5): 14 SNPs by restriction fragment length polymorphism analysis and 5
SNPs by unlabelled probe melting analysis [39-43]. Details
of primer sequences and reaction conditions are given in
(Additional file 6: Table S3). For illustration, the restriction
fragments and the banding patterns of a SNP are shown
in (Additional file 7: Figure S7), and the melting curves of
another SNP in (Additional file 8: Figure S8).
Imputation of genotypes for 76 JAK2 SNPs

Genotypes of 76 additional SNPs within the 148.7-kb region under study were imputed by Beagle v3.2 [44]. One

of the imputed SNPs was rs4495487, which was recently
reported to contribute to MPN development in the Japanese population [14]. The genotype data of the 1000 Genomes Project (phase 1) based on 97 CHB subjects were
used as the reference panel. We manually performed a
quality control check by removing some of the known
genotypes of the 19 directly genotyped SNPs, and imputed them with Beagle v3.2. The post-imputation results were merged with the original data to check for the
imputation accuracy.
Statistical analysis

Genotypes were tested for Hardy-Weinberg equilibrium
(HWE) by Fisher’s exact test using PLINK (ver.1.07) [25]
prior to data analysis. PLINK was used for statistical
analysis for all the 19 directly genotyped SNPs and the
76 imputed SNPs, and also the haplotype association
tests. Single-marker and haplotype analyses were conducted between cases and controls with logistic regression adjusted for sex and age (age at diagnosis for MPN
patients) as covariates; the respective asymptotic P value
was denoted as Pasym. Correction for multiple comparisons was achieved by generating empirical P values
(Pemp) based on 50,000 permutations, i.e., swapping of
the case–control status 50,000 times. Haplotypes were
defined by a variable-sized sliding-window approach
based on all possible sizes of SNPs spanning the whole genomic region. Subsequently, we studied the contribution of


Koh et al. BMC Genetics 2014, 15:147
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individual SNPs to significant haplotype association with
disease by conditional logistic regression analysis. Haploview v4.2 [45] was used to generate the linkage disequilibrium (LD) map of the JAK2 gene based on an algorithm
called solid spine of linkage disequilibrium (SSLD) [45].

Page 11 of 12


Authors’ contributions
Yip SP designed the research plan, analysed the data, and wrote and revised
the manuscript. Koh SP performed experiments, analysed the data, and
wrote and revised the manuscript. Lee KK, Chan CC, Lau SM, Kho CS, Lau CK,
Lau YM, Lin SY, Wong LG, Au KL, Wong KF, Chu W, Yu PH, Chow ED and
Leung KFS recruited and diagnosed the cases. Tsoi WC collected and
provided data of the controls. Yung YM revised the manuscript for
intellectual content. All authors read and approved the final manuscript.

Additional files
Additional file 1: Table S1. Allelic association tests for 19 genotyped
SNPs of the JAK2 gene in V617F-negative MPNs.
Additional file 2: Figure S1. Haploview-generated linkage
disequilibrium (LD) map of 19 JAK2 SNPs in Han Chinese in Beijing (A)
and Caucasians of European ancestry (B) based on the 1000 Genomes
Project data. LD plots were generated utilising the Haploview software.
The values in the boxes indicate the r2 values between the respective
pairs of SNPs and the empty boxes represent those with r2 = 1.0.
Haplotype blocks are defined by solid spine of linkage disequilibrium.
Additional file 3: Figure S2. Haploview-generated linkage disequilibrium
map of 19 SNPs in the JAK2 gene for only the 470 controls of our study. Figure S3. Haploview-generated linkage disequilibrium map of the 19 SNPs in
the JAK2 gene for 44 V617F-negative MPN cases and 470 controls of our
study. Figure S4. Haploview-generated linkage disequilibrium map of the
19 SNPs in the JAK2 gene for all the 172 V617F-positive and -negative MPNs
patients and the 470 controls of our study. Figure S5. Haploview-generated
linkage disequilibrium map of the 19 SNPs in the JAK2 gene for the 172
V617F-positive and -negative MPNs patients of our study. Figure S6.
Haploview-generated linkage disequilibrium map of the 19 SNPs in the JAK2
gene for the 128 V617F-positive MPNs patients of our study.
Additional file 4: Table S2. Summary of exhaustive haplotype analyses

based on age- and sex-adjusted omnibus tests for sliding windows of
up to 19 SNPs per window for 95 genotyped/imputed JAK2 SNPs for
V617F-positive MPNs.
Additional file 5: Genotyping of JAK2 mutation and single nucleotide
polymorphisms (SNPs).
Additional file 6: Table S3. JAK2 SNPs: Genotyping methods, primers,
probes and key reaction conditions for PCR.
Additional file 7: Figure S7. SNP genotyping by restriction fragment
length polymorphism. For illustration, rs10119004 (S10) is used as an
example. A PCR fragment of 244 bp (see Additional file; Table S3) is
amplified to encompass the SNP site. Upper panel: Restriction patterns
for the two alleles of rs10119004 (S10) upon restriction digestion by the
restriction enzyme HphI. Lower panel: Electrophoresis banding patterns
on 8% polyacrylamide gel and stained with SYBR Green I. The DNA
ladder is the 1 kb Plus DNA Ladder (lane M) from Invitrogen Life
Technologies.
Additional file 8: Figure S8. SNP genotyping by unlabelled probe
melting analysis. For illustration, rs7849191 (S2) is used as an example. A
PCR fragment of 115 bp (see Additional file 6: Table S3) is amplified to
encompass the SNP site. The unlabelled probe is designed to match the
C allele in this example. The probe-amplicon duplex of the homozygous
genotype CC has a higher melting temperature than the probe-amplicon
duplex of the homozygous genotype TT. Two peaks are obtained for the
heterozygous genotype CT.

Abbreviations
SNP: Single nucleotide polymorphism; JAK2: Janus kinase 2;
MPN: Myeloproliferative neoplasm; PV: Polycythaemia vera; ET: Essential
thromocythaemia; PMF: Primary myelofibrosis; LD: Linkage disequilibrium;
MAF: Minor allele frequency; OR: Odds ratio; CEU: Caucasians with European

ancestry; CHB: Han Chinese in Beijing; SSLD: Solid spine of linkage
disequilibrium.
Competing interests
The authors declare that they have no competing interests.

Acknowledgements
This study was supported by grants from the Hong Kong Polytechnic
University.
Author details
1
Department of Health Technology and Informatics, The Hong Kong
Polytechnic University, Hong Kong, China. 2Departments of Medicine,
Princess Margaret Hospital, Hong Kong, China. 3Department of Medicine,
Queen Elizabeth Hospital, Hong Kong, China. 4Department of Medicine,
Pamela Youde Nethersole Eastern Hospital, Hong Kong, China. 5Department
of Medicine, Tseung Kwan O Hospital, Hong Kong, China. 6Department of
Medicine & Geriatrics, United Christian Hospital, Hong Kong, China.
7
Departments of Pathology, Princess Margaret Hospital, Hong Kong, China.
8
Departments of Pathology, Queen Elizabeth Hospital, Hong Kong, China.
9
Departments of Pathology, Pamela Youde Nethersole Eastern Hospital,
Hong Kong, China. 10Departments of Pathology, Tseung Kwan O Hospital,
Hong Kong, China. 11Departments of Pathology, United Christian Hospital,
Hong Kong, China. 12Departments of Pathology, North District Hospital,
Hong Kong, China. 13Hong Kong Red Cross Blood Transfusion Service, Hong
Kong, China.
Received: 6 April 2014 Accepted: 8 December 2014


References
1. Tefferi A, Vardiman JW: Classification and diagnosis of myeloproliferative
neoplasms: the 2008 World Health Organization criteria and point-ofcare diagnostic algorithms. Leukemia 2008, 22(1):14–22.
2. Tefferi A: Novel mutations and their functional and clinical relevance in
myeloproliferative neoplasms: JAK2, MPL, TET2, ASXL1, CBL, IDH and
IKZF1. Leukemia 2010, 24(6):1128–1138.
3. Levine RL, Wadleigh M, Cools J, Ebert BL, Wernig G, Huntly BJ, Boggon TJ,
Wlodarska I, Clark JJ, Moore S, Adelsperger J, Koo S, Lee JC, Gabriel S,
Mercher T, D'Andrea A, Frohling S, Dohner K, Marynen P, Vandenberghe P,
Mesa RA, Tefferi A, Griffin JD, Eck MJ, Sellers WR, Meyerson M, Golub TR,
Lee SJ, Gilliland DG: Activating mutation in the tyrosine kinase JAK2 in
polycythemia vera, essential thrombocythemia, and myeloid metaplasia
with myelofibrosis. Cancer Cell 2005, 7(4):387–397.
4. Jones AV, Kreil S, Zoi K, Waghorn K, Curtis C, Zhang L, Score J, Seear R,
Chase AJ, Grand FH, White H, Zoi C, Loukopoulos D, Terpos E, Vervessou EC,
Schultheis B, Emig M, Ernst T, Lengfelder E, Hehlmann R, Hochhaus A,
Oscier D, Silver RT, Reiter A, Cross NC: Widespread occurrence of the JAK2
V617F mutation in chronic myeloproliferative disorders. Blood 2005,
106(6):2162–2168.
5. Baxter EJ, Scott LM, Campbell PJ, East C, Fourouclas N, Swanton S, Vassiliou
GS, Bench AJ, Boyd EM, Curtin N, Scott MA, Erber WN, Green AR: Acquired
mutation of the tyrosine kinase JAK2 in human myeloproliferative
disorders. Lancet 2005, 365(9464):1054–1061.
6. James C, Ugo V, Le Couedic JP, Staerk J, Delhommeau F, Lacout C, Garcon
L, Raslova H, Berger R, Bennaceur-Griscelli A, Villeval JL, Constantinescu SN,
Casadevall N, Vainchenker W: A unique clonal JAK2 mutation leading to
constitutive signalling causes polycythaemia vera. Nature 2005,
434(7037):1144–1148.
7. Kralovics R, Passamonti F, Buser AS, Teo SS, Tiedt R, Passweg JR, Tichelli A, Cazzola
M, Skoda RC: A gain-of-function mutation of JAK2 in myeloproliferative

disorders. N Engl J Med 2005, 352(17):1779–1790.
8. Landgren O, Goldin LR, Kristinsson SY, Helgadottir EA, Samuelsson J,
Bjorkholm M: Increased risks of polycythemia vera, essential
thrombocythemia, and myelofibrosis among 24,577 first-degree relatives
of 11,039 patients with myeloproliferative neoplasms in Sweden. Blood
2008, 112(6):2199–2204.


Koh et al. BMC Genetics 2014, 15:147
/>
9.

10.

11.

12.

13.

14.

15.

16.

17.

18.


19.

20.

21.

22.

23.

24.
25.

26.

27.

Olcaydu D, Harutyunyan A, Jager R, Berg T, Gisslinger B, Pabinger I,
Gisslinger H, Kralovics R: A common JAK2 haplotype confers susceptibility
to myeloproliferative neoplasms. Nat Genet 2009, 41(4):450–454.
Jones AV, Chase A, Silver RT, Oscier D, Zoi K, Wang YL, Cario H, Pahl HL,
Collins A, Reiter A, Grand F, Cross NC: JAK2 haplotype is a major risk factor
for the development of myeloproliferative neoplasms. Nat Genet 2009,
41(4):446–449.
Kilpivaara O, Mukherjee S, Schram AM, Wadleigh M, Mullally A, Ebert BL,
Bass A, Marubayashi S, Heguy A, Garcia-Manero G, Kantarjian H, Offit K, Stone
RM, Gilliland DG, Klein RJ, Levine RL: A germline JAK2 SNP is associated with
predisposition to the development of JAK2(V617F)-positive myeloproliferative
neoplasms. Nat Genet 2009, 41(4):455–459.
Pardanani A, Lasho TL, Finke CM, Gangat N, Wolanskyj AP, Hanson CA,

Tefferi A: The JAK2 46/1 haplotype confers susceptibility to essential
thrombocythemia regardless of JAK2V617F mutational status-clinical
correlates in a study of 226 consecutive patients. Leukemia 2010,
24(1):110–114.
Tefferi A, Lasho TL, Patnaik MM, Finke CM, Hussein K, Hogan WJ, Elliott MA,
Litzow MR, Hanson CA, Pardanani A: JAK2 germline genetic variation
affects disease susceptibility in primary myelofibrosis regardless of
V617F mutational status: nullizygosity for the JAK2 46/1 haplotype is
associated with inferior survival. Leukemia 2010, 24(1):105–109.
Ohyashiki JH, Yoneta M, Hisatomi H, Iwabuchi T, Umezu T, Ohyashiki K: The
C allele of JAK2 rs4495487 is an additional candidate locus that
contributes to myeloproliferative neoplasm predisposition in the
Japanese population. BMC Med Genet 2012, 13:6.
Tanaka M, Yujiri T, Ito S, Okayama N, Takahashi T, Shinohara K, Azuno Y,
Nawata R, Hinoda Y, Tanizawa Y: JAK2 46/1 haplotype is associated with
JAK2 V617F-positive myeloproliferative neoplasms in Japanese patients.
Int J Hematol 2013, 97(3):409–413.
Pardanani A, Fridley BL, Lasho TL, Gilliland DG, Tefferi A: Host genetic
variation contributes to phenotypic diversity in myeloproliferative
disorders. Blood 2008, 111(5):2785–2789.
Trifa AP, Cucuianu A, Petrov L, Urian L, Militaru MS, Dima D, Pop IV,
Popp RA: The G allele of the JAK2 rs10974944 SNP, part of JAK2 46/1
haplotype, is strongly associated with JAK2 V617F-positive myeloproliferative
neoplasms. Ann Hematol 2010, 89(10):979–983.
Wang K, Swierczek S, Hickman K, Hakonarson H, Prchal JT: Convergent
mechanisms of somatic mutations in polycythemia vera. Discov Med
2011, 12(62):25–32.
Hu TT, Zhang XJ, Guan M: The association between jak2 46/1 haplotype
and the susceptibility of PV and ET in Chinese Han population.
Zhonghua Yixue Jianyan Zazhi 2011, 34(8):717–721.

Zhang X, Hu T, Wu Z, Kang Z, Liu W, Guan M: The JAK2 46/1 haplotype is
a risk factor for myeloproliferative neoplasms in Chinese patients. Int J
Hematol 2012, 96(5):611–616.
Wang J, Xu Z, Liu L, Gale RP, Cross NC, Jones AV, Qin T, Ai X, Xu J, Zhang T,
Sun X, Li Q, Zhang P, Zhang Y, Xiao Z: JAK2V617F allele burden, JAK2 46/1
haplotype and clinical features of Chinese with myeloproliferative
neoplasms. Leukemia 2013, 27(8):1763–1767.
Hsiao HH, Liu YC, Tsai HJ, Lee CP, Hsu JF, Lin SF: JAK2V617F mutation is
associated with special alleles in essential thrombocythemia. Leuk
Lymphoma 2011, 52(3):478–482.
Pagliarini-e-Silva S, Santos BC, Pereira EM, Ferreira ME, Baraldi EC, Sell AM,
Visentainer JE: Evaluation of the association between the JAK2 46/1
haplotype and chronic myeloproliferative neoplasms in a Brazilian
population. Clinics 2013, 68(1):5–9.
The International HapMap Consortium: The International HapMap Project.
Nature 2003, 426(6968):789–796.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J,
Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for wholegenome association and population-based linkage analyses. Am J Hum
Genet 2007, 81(3):559–575.
Guo Y, Li J, Bonham AJ, Wang Y, Deng H: Gains in power for exhaustive
analyses of haplotypes using variable-sized sliding window strategy: a
comparison of association-mapping strategies. Eur J Hum Genet 2009,
17(6):785–792.
Zerjavic K, Zagradisnik B, Lokar L, Krasevac MG, Vokac NK: The association
of the JAK2 46/1 haplotype with non-splanchnic venous thrombosis.
Thromb Res 2013, 132(2):e86–e93.

Page 12 of 12

28. Zaykin DV, Meng Z, Ehm MG: Contrasting linkage-disequilibrium patterns

between cases and controls as a novel association-mapping method.
Am J Hum Genet 2006, 78(5):737–746.
29. Shifman S, Kuypers J, Kokoris M, Yakir B, Darvasi A: Linkage disequilibrium
patterns of the human genome across populations. Hum Mol Genet 2003,
12(7):771–776.
30. Dayem Ullah AZ, Lemoine NR, Chelala C: SNPnexus: a web server for
functional annotation of novel and publicly known genetic variants
(2012 update). Nucleic Acids Res 2012, 40(Web Server issue):W65–W70.
31. Xu Z, Taylor JA: SNPinfo: integrating GWAS and candidate gene
information into functional SNP selection for genetic association studies.
Nucleic Acids Res 2009, 37(Web Server issue):W600–W605.
32. Lee PH, Shatkay H: F-SNP: computationally predicted functional SNPs for
disease association studies. Nucleic Acids Res 2008, 36(Database issue):
D820–D824.
33. Cartharius K, Frech K, Grote K, Klocke B, Haltmeier M, Klingenhoff A,
Frisch M, Bayerlein M, Werner T: MatInspector and beyond: promoter
analysis based on transcription factor binding sites. Bioinformatics 2005,
21(13):2933–2942.
34. eQTL resources @ the Pritchard lab [ />gbrowse/eqtl/]
35. Xia K, Shabalin AA, Huang S, Madar V, Zhou YH, Wang W, Zou F, Sun W,
Sullivan PF, Wright FA: seeQTL: a searchable database for human eQTLs.
Bioinformatics 2012, 28(3):451–452.
36. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM,
Haussler D: The human genome browser at UCSC. Genome Res 2002,
12(6):996–1006.
37. Flutre T, Wen X, Pritchard J, Stephens M: A statistical framework for joint
eQTL analysis in multiple tissues. PLoS Genet 2013, 9(5):e1003486.
38. Purcell S, Cherny SS, Sham PC: Genetic power calculator: design of linkage
and association genetic mapping studies of complex traits. Bioinformatics
2003, 19(1):149–150.

39. Zhou L, Myers AN, Vandersteen JG, Wang L, Wittwer CT: Closed-tube
genotyping with unlabeled oligonucleotide probes and a saturating
DNA dye. Clin Chem 2004, 50(8):1328–1335.
40. Jiang B, Yap MK, Leung KH, Ng PW, Fung WY, Lam WW, Gu YS, Yip SP:
PAX6 haplotypes are associated with high myopia in Han Chinese.
PLoS One 2011, 6(5):e19587.
41. Mak JY, Yap MK, Fung WY, Ng PW, Yip SP: Association of IGF1 gene
haplotypes with high myopia in Chinese adults. Arch Ophthalmol 2012,
130(2):209–216.
42. Zhu MM, Yap MK, Ho DW, Fung WY, Ng P, Gu YS, Yip SP: Investigating the
relationship between UMODL1 gene polymorphisms and high myopia: a
case–control study in Chinese. BMC Med Genet 2012, 13:64.
43. Yiu WC, Yap MK, Fung WY, Ng PW, Yip SP: Genetic susceptibility to
refractive error: association of vasoactive intestinal peptide receptor 2
(VIPR2) with high myopia in Chinese. PLoS One 2013, 8(4):e61805.
44. Browning BL, Browning SR: A unified approach to genotype imputation
and haplotype-phase inference for large data sets of trios and unrelated
individuals. Am J Hum Genet 2009, 84(2):210–223.
45. Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of
LD and haplotype maps. Bioinformatics 2005, 21(2):263–265.
doi:10.1186/s12863-014-0147-y
Cite this article as: Koh et al.: Genetic association between germline
JAK2 polymorphisms and myeloproliferative neoplasms in Hong Kong
Chinese population: a case–control study. BMC Genetics 2014 15:147.